检索结果-南通市图书馆

Journal of Systems Science & Complexity 2024年第5期37卷 2173-2195页

作者： LI Meiqi jin baisuo School of Management University of Science and Technology of ChinaHefei 230000China

Negative binomial regression is a powerful technique for modeling count data,particularly when dealing with ***,estimating the parameters for large-dimensional sparse models is challenging due to the complexity of optimizing the mean and dispersion parameter of the negative binomial *** address this issue,the authors propose a novel approach that employs two iterations of the majorize-minimize(MM)algorithm,one for estimating the dispersion parameter and the other for estimating the mean *** approaches improve the convergence speed and stability of the *** authors also use group penalty for variable selection,which enhances the accuracy and efficiency of the *** proposed method provides an explicit solution,simplifies the iteration process,and maintains good stability while ensuring algorithm ***,the authors apply the proposed algorithm to the zero-inflated model and demonstrate its promising predictive performance on specific data *** research has important implications for count data modeling and analysis in various fields,such as data mining,machine learning,and bioinformatics.

关键词： Group structure majorize-minimize(MM) negative binomial

在线全文

学校读者我要写书评

暂无评论

多阈值负二项回归模型

应用概率统计 2024年第4期40卷 684-696页

作者：廖红怡金百锁中国科学技术大学管理学院统计与金融系合肥230026

本文提出了一个多阈值负二项回归模型并使用一个带有迭代过程的两阶段程序来估计阈值的位置和模型参数.在第一阶段,我们使用分组选择的方法识别出阈值的数量,然后在第二阶段得到阈值的位置和模型参数的估计.我们通过模拟研究来检验所提... 详细信息

本文提出了一个多阈值负二项回归模型并使用一个带有迭代过程的两阶段程序来估计阈值的位置和模型参数.在第一阶段,我们使用分组选择的方法识别出阈值的数量,然后在第二阶段得到阈值的位置和模型参数的估计.我们通过模拟研究来检验所提出方法的性能,并使用一个真实交通事故数据进行说明.

关键词：阈值负二项回归两阶段程序

在线全文

学校读者我要写书评

暂无评论

基于自回归模型的社交网络依赖性检验

中国科学：数学 2024年第1期54卷 75-86页

作者：金百锁李雯仪王学钦中国科学技术大学管理学院合肥230026 中国科学技术大学国际金融研究院合肥230601

随着信息技术的迅速发展,社交网络对人们生活的影响日益显著,对各种社交网络的分析和研究也显得越来越重要.识别和确定社交依赖性是社交网络分析的主要目标.本文提出一个改进的空间自回归模型,引入一个易感性指标对个体社交网络依赖性... 详细信息

随着信息技术的迅速发展,社交网络对人们生活的影响日益显著,对各种社交网络的分析和研究也显得越来越重要.识别和确定社交依赖性是社交网络分析的主要目标.本文提出一个改进的空间自回归模型,引入一个易感性指标对个体社交网络依赖性进行分类.基于本文所提出的模型,本文构造一个用于检验社交网络依赖是否存在的得分检验统计量,给出在原假设和备择假设下的检验统计量的渐近分布.当社交网络依赖性存在时,可以得到易感性指标的参数估计.本文通过模拟来评估所提出的检验统计量的经验表现.最后应用本文所提出的方法分析一个面向视频游戏的实时流媒体视频平台的数据集.

关键词：空间自回归社交网络依赖性变平面分析得分检验

Outlier Detection via a Block Diagonal Product Estimator

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science & Complexity 2022年第5期35卷 1929-1943页

作者： LI Chikun jin baisuo School of Management University of Science and Technology of ChinaHefei 230026China

Outlier detection is a fundamental topic in robust *** outlier detection methods try to find a clean subset of given size,which is used to estimate the location vector and scatter matrix,and the outliers can be flagged by the Mahalanobis ***,methods such as the minimum covariance determinant approach cannot be applied directly to high-dimensional data,especially when the dimension of the sample is greater than the sample size.A novel fast detection procedure based on a block diagonal partition is proposed,and the asymptotic distribution of the modified Mahalanobis distance is *** authors verify the specificity and sensitivity of this procedure by simulation and real data analysis in high-dimensional settings.

关键词： Block diagonal high dimension minimum covariance determinant estimator

Robust Two-Stage Estimation in General Spatial Dynamic Panel Data Models

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science & Complexity 2023年第6期36卷 2580-2604页

作者： DING Hao jin baisuo WU Yuehua Department of Mathematics and Statistics York UniversityTorontoOntarioM3J 1P3Canada Department of Statistics and Finance School of ManagementUniversity of Science and Technology of ChinaHefei230026China

This paper proposes a robust two-stage estimation procedure for a general spatial dynamic panel data model in light of the two-stage estimation procedure in jin,et al.(2020).The authors replace the least squares estimation in the first stage of jin,et al.(2020)by *** authors also provide the justification for not making any change in its second stage when the number of time periods is large *** proposed methodology is robust and efficient,and it can be easily *** addition,the authors study the limiting behavior of the parameter estimators,which are shown to be consistent and asymptotic normally distributed under some *** simulation studies are carried out to assess the proposed procedure and a COVID-19 data example is conducted for illustration.

关键词： Asymptotic normality consistency M-estimation model selection outliers

Pairwise Fusion Approach Incorporating Prior Constraint Information

在线全文

学校读者我要写书评

暂无评论

Communications in Mathematics and Statistics 2020年第1期8卷 47-62页

作者： Yaguang Li baisuo jin School of Management University of Science and Technology of ChinaHefei 230026China

In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint *** sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneity means that regression coefficients are grouped and have exactly the same value in each group.A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex *** develop a modified alternating direction method of multipliers algorithm to obtain the estimators and demonstrate its *** efficiency of both sparsity and homogeneity detection can be improved by combining the prior *** proposed method is further illustrated by simulation studies and analysis of an ozone dataset.

关键词： Alternating direction method of multipliers Prior constraint information Sparsity Homogeneity Linear regression

Consistent tuning parameter selection in high-dimensional group-penalized regression

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China Mathematics 2019年第4期62卷 751-770页

作者： Yaguang Li Yaohua Wu baisuo jin School of Management University of Science and Technology of China

Various forms of penalized estimators with good statistical and computational properties have been proposed for variable selection respecting the grouping structure in the variables. The attractive properties of these shrinkage and selection estimators, however, depend critically on the choice of the tuning *** method for choosing the tuning parameter is via information criteria, such as the Bayesian information criterion(BIC). In this paper, we consider the problem of consistent tuning parameter selection in high dimensional generalized linear regression with grouping structures. We extend the results of the extended regularized information criterion(ERIC) to group selection methods involving concave penalties and then investigate the selection consistency with diverging variables in each group. Moreover, we show that the ERIC-type selector enables consistent identi?cation of the true model and that the resulting estimator possesses the oracle property even when the number of group is much larger than the sample size. Simulations show that the ERIC-type selector can signi?cantly outperform the BIC and cross-validation selectors when choosing true grouped variables,and an empirical example is given to illustrate its use.

关键词： Bayesian information criterion group selection penalized likelihood regularization parameter ultra-high dimensionality

SELECTING AN ADAPTIVE SEQUENCE FOR COMPUTING RECURSIVE M-ESTIMATORS IN MULTIVARIATE LINEAR REGRESSION MODELS

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science & Complexity 2013年第4期26卷 583-594页

作者： MIAO Baiqi TONG Qian WU Yuehua jin baisuo Department of Statistics and Finance University of Science and Technology of China Department of Mathematics and Statistics York UniversityTorontoOntarioM3J1P3Canada

In this paper, the authors consider an adaptive recursive algorithm by selecting an adaptive sequence for computing M-estimators in multivariate linear regression models. Its asymptotic property is investigated. The recursive algorithm given by Miao and Wu （1996） is modified accordingly. Simu- lation studies of the Mgorithm is also provided. In addition, the Newton-Raphson iterative algorithm is considered for the purpose of comparison.

关键词： Adaptive sequence M-estimation multivariate linear model recursive algorithm scatter parameters.

On high-dimensional change point problem

在线全文

学校读者我要写书评

暂无评论

Science China Mathematics 2016年第12期59卷 2355-2378页

作者： jin baisuo PAN GuangMing YANG Qing ZHOU Wang School of Management University of Science and Technology of China School of Physical and Mathematical Sciences Nanyang Technological University Department of Statistics and Applied Probability National University of Singapore

New statistics are proposed to estimate and test the structural change when the data dimension is comparable to or larger than the sample size. Consistency of the new statistic in estimating the change point position is established under the alternative hypothesis. The asymptotic distribution of the new statistic in testing the existence of a change point is obtained under the null hypothesis. Some simulation results are presented which show that the numerical performance of our method is satisfactory. The method is illustrated via the analysis of the house price index of US.

关键词： change point high-dimensional statistics inference Hotelling's T^2 statistic

Evaluation of the Canadian government policies on controlling the COVID-19 outbreaks

在线全文

学校读者我要写书评

暂无评论

Statistical Theory and Related Fields 2023年第3期7卷 223-234页

作者： Mengyao Chen Yuehua Wu baisuo jin Department of Statistics and Finance University of Science and Technology of ChinaAnhuiPeople's Republic of China Department of Mathematics and Statistics York UniversityTorontoCanada

In this paper,we investigate the COVID-19 pandemic in Canada and evaluate the Canadian gov-ernment policies on controlling COVID-19 *** first case of COVID-19 was reported in Ontario on 25 January *** then,there have been over million cases by *** this time period,the federal,provincial and local governments have implemented regulations and policies in order to control the *** evaluate these government policies,which may be done by analysing the infection rate,infection period and reproductive number of COVID-19,we approach the problem by introducing an extended susceptible-exposed-infectious-removed(SEIR)model and conduct the model inference by using the iterated filter ensemble adjustment Kalman filter(IF-EAKF)*** first divide the time period into phases according to the policy intensities in each province by segmenting the time period from 4 March 2020 to 31 Octo-ber 2020 into three time phases:the exploding phase,the strict policy implementation phase,and the provincial reopening *** then use IF-EAKF algorithm to obtain the estimates of the model *** show that the infection rate in the second phase is lower than that in both first and third *** also discuss the number of new COVID-19 cases under different policy intensities and different policy durations in the third wave of the pandemic.

关键词： IF-EAKF policy implementation reproductive number prediction